An example usage of `strymread`¶

Analyzing CAN data logged from Giraffee and Panda¶

Prerequisite¶

Install strym. See documentation for installation instruction: https://jmscslgroup.github.io/strym/installation.html

We will read the CAN data file which is recorded via libpanda using comma.ai Panda devices. You must supply a DBC file for decoding hex messes. If you don’t provide DBC file, strymread defaults to using RAV4 2019 DBC file which is pre-packaged with strym. In this example, we won’t be supplying DBC file as data that we are going to read is from Toyota RAV4. strymread will use default DBC file. Further, naming of CAN CSV file must have VIN number and strymread will guess whihc DBC file to use.

[1]:

import strym
from strym import strymread
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
csvfile = '../PandaData/2020_06_01/2020-06-01-13-01-36_2T3Y1RFV8KC014025_CAN_Messages.csv'
r =strymread(csvfile=csvfile)

Loading BokehJS ...

We will visualize the counts of all messages¶

The plot is split into several subplots for brevity. The function returns a count dataframe with Message count by BUS IDs and total counts

[2]:

count = r.count(plot=True)

/home/ivory/anaconda3/envs/dbn/lib/python3.7/site-packages/strym-0.3.5-py3.7.egg/strym/strymread.py:553: UserWarning:

Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.

[3]:

count

[3]:

	MessageID	Counts_Bus_0	Counts_Bus_1	Counts_Bus_2	TotalCount
36	36	54926	0	54926	109852
37	37	109852	0	109852	219704
170	170	109852	0	109852	219704
180	180	54926	0	54926	109852
186	186	36617	0	36617	73234
...	...	...	...	...	...
1990	1990	45	0	45	90
1994	1994	27	0	27	54
1998	1998	45	0	45	90
2004	2004	52	0	52	104
2012	2012	52	0	52	104

209 rows × 5 columns

Let’s plot a few important data¶

Speed¶

We first make static plot by setting strym.config['interactive']=False

[4]:

strym.config['interactive']=False
speed = r.speed()
strymread.plt_ts(speed, title="Speed Plot")

/home/ivory/anaconda3/envs/dbn/lib/python3.7/site-packages/strym-0.3.5-py3.7.egg/strym/strymread.py:2855: UserWarning:

Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.

Now, we create interactive plot

[6]:

strym.config['interactive']=True
speed = r.speed()
strymread.plt_ts(speed, title="Speed Plot")

Lets visualize a message with given message ID and signal ID¶

[7]:

msg869 = r.get_ts(869, 6)
msg869

[7]:

	Time	Message	Bus
Clock
2020-06-01 20:01:37.706951857	1.591042e+09	252	2
2020-06-01 20:01:37.706951857	1.591042e+09	252	0
2020-06-01 20:01:37.727082968	1.591042e+09	252	2
2020-06-01 20:01:37.727082968	1.591042e+09	252	0
2020-06-01 20:01:37.747102022	1.591042e+09	252	2
...	...	...	...
2020-06-01 20:19:54.863569021	1.591043e+09	2	0
2020-06-01 20:19:55.064127922	1.591043e+09	2	2
2020-06-01 20:19:55.064127922	1.591043e+09	2	0
2020-06-01 20:19:55.264127970	1.591043e+09	2	2
2020-06-01 20:19:55.264127970	1.591043e+09	2	0

10984 rows × 3 columns

[8]:

strymread.plt_ts(msg869, title="Message 869, Signal 6")

Rate Statistics for every message ID¶

[9]:

u = r.frequency()
u

[9]:

	MessageID	MeanRate	MedianRate	RateStd	MaxRate	MinRate	RateIQR
0	36	457.418698	50.127928	1461.255929	12985.461300	11.852837	1.420878
1	37	518.697935	100.563537	1445.840868	11618.570637	13.396566	5.826275
2	170	390.135893	100.301409	1224.572556	11522.813187	13.373116	4.687697
3	180	546.361381	50.153103	1565.040143	12192.744186	12.956059	1.372874
4	186	418.632506	33.459407	1432.105869	11491.243836	11.474896	1.259073
...	...	...	...	...	...	...	...
204	1990	15.867022	10.013666	16.021242	72.290658	0.004203	7.691075
205	1994	785.949826	67.864416	1885.388254	6574.144201	0.033738	102.817044
206	1998	155.187068	10.118899	919.100583	6250.825633	0.004203	14.288731
207	2004	434.944156	10.023070	1506.322766	6061.132948	0.017528	18.321202
208	2012	514.649557	10.551886	1621.569966	6403.517557	0.017533	13.465362

209 rows × 7 columns

Synchronize Two Time Series messages and resample with a fixed datarate¶

It means first time of speed is earlier than yaw in time-series data so we have to interpolate speed value at yaw’s first time. We will use linear interpolation.

Linear interpolation formula is

\[X_i = \cfrac{X_A - X_B}{a-b}(i-b) + X_B\]

[10]:

ts_yaw_rate = r.yaw_rate()
ts_speed = r.speed()

# integrate yaw rate to get the heading
ts_yaw = strymread.integrate(ts_yaw_rate)

[11]:

strymread.plt_ts(ts_yaw, title="Yaw")

We will perform synchornize two timeseries messages so that they both can have same time points. By default, when time-synchronizing, sample rate is 50 Hz. But you can specify some other rates. However, for a good quality synchronization, desired sample rate is recommended to be lower than equal to of minimum of average sample rates of two timeseries.¶

[12]:

interpolated_speed, interpolated_yaw = strymread.ts_sync(ts_speed, ts_yaw, rate = 20)

[13]:

strymread.plt_ts(interpolated_speed, title="Interpolated Speed")
strymread.plt_ts(ts_speed, title="Original Speed")

[14]:

strymread.plt_ts(interpolated_yaw, title="Interpolated Yaw")
strymread.plt_ts(ts_yaw, title="Original Yaw")

Plot the trajectory of vehice based on kinematic model using yaw rate and speed¶

[15]:

T = r.trajectory()

[16]:

fig, ax = strymread.create_fig(1)
ax[0].scatter(x = 'X', y = 'Y', c = 'Time', data = T, s = 1)
ax[0].set_title("Vehicle's Trajectory")
plt.show()

[17]:

T[1:7500]

[17]:

	Time	X	Y	Vx	Vy
1	1.591042e+09	0.076889	0.000001	3.844444	0.000055
2	1.591042e+09	0.159306	0.000071	4.120851	0.003512
3	1.591042e+09	0.246237	0.000209	4.346545	0.006890
4	1.591042e+09	0.336663	0.000399	4.521301	0.009474
5	1.591042e+09	0.429092	0.000633	4.621473	0.011701
...	...	...	...	...	...
7495	1.591042e+09	1505.749024	99.366560	16.675783	-1.306036
7496	1.591042e+09	1506.082129	99.340442	16.655216	-1.305881
7497	1.591042e+09	1506.414734	99.314334	16.630274	-1.305411
7498	1.591042e+09	1506.746662	99.288252	16.596424	-1.304107
7499	1.591042e+09	1507.078212	99.262189	16.577464	-1.303143

7499 rows × 5 columns

Get the meta data about driving¶

driving_characteristics function will give dictionary formatted meta data of the drive being analyzed.

[18]:

metadata = r.driving_characteristics()

[19]:

metadata

[19]:

{'filename': '../PandaData/2020_06_01/2020-06-01-13-01-36_2T3Y1RFV8KC014025_CAN_Messages.csv',
 'dbcfile': '/home/ivory/anaconda3/envs/dbn/lib/python3.7/site-packages/strym-0.3.5-py3.7.egg/strym/dbc/toyota_rav4_2019.dbc',
 'distance_meters': 14631.19081597858,
 'distance_km': 14.631190815978579,
 'distance_miles': 9.09142307777013,
 'start_time': 'Mon Jun  1 13:01:37 2020',
 'end_time': 'Mon Jun  1 13:19:55 2020',
 'trip_time': 1097.749980211258}

Get the distribution of data¶

We will show the distribution of speed data¶

[20]:

speed = r.speed()
strymread.violinplot(speed['Message'], title="Speed Data")

/home/ivory/anaconda3/envs/dbn/lib/python3.7/site-packages/seaborn/_core.py:1296: UserWarning:

Horizontal orientation ignored with only `y` specified.

Rate Analysis for specific messages¶

Let’s say we want to analyze data throughput aka rate for Radar data. For this specific example, we will be looking at TRACK_A_0. For that we will call the appropriate function to retreive longitudinal data corresponding to TRACK_A_0.

[21]:

long_dist = r.long_dist(track_id=0) # I want to analyze rate for TRACK_A_0 only
long_dist
strymread.ranalyze(long_dist, title='Longitudinal Distance Data: TRACK A 0')

Analyzing Timestamp and Data Rate of Longitudinal Distance Data: TRACK A 0
Interquartile Range of Rate for Longitudinal Distance Data: TRACK A 0 is 0.18910648197294577

The above plot shows that, we receive RADAR data on TRACK A 0 at roughly 20 Hz in more or less consistent manner.

Extract Data for use in MATLAB¶

Often we want to extract data for analysis in MATLAB or let say your collaborator is not well-versed in Python but want data suitable for Analysis in MATLAB, in that case, we can extract data for use in MATLAB and it will save the data as .mat file.

[ ]:

files = r.export2mat(force_rewrite=True) #force_rewrite overwrites an already existing mat file if you had one previously

The mat file save has the same name as that of original csvfile passed to strymread object but with extension .mat

An example usage of strymread¶